Method and system for measuring lactate levels in vivo

ABSTRACT

There is described a system and method for the in vivo determination of lactate levels in blood using Near-Infrared Spectroscopy (NIRS) and/or Near-infrared Raman Spectroscopy (NIR-RAMAN). The method teaches measuring lactate in vivo comprising: optically coupling a body part with a light source and a light detector the body part having tissues comprising blood vessels; injecting near-infrared (NIR) light at one or a plurality of wavelengths in the body part; detecting, as a function of blood volume variations in the body part, light exiting the body part at at least the plurality of wavelengths to generate an optical signal; and processing the optical signal as a function of the blood volume variations to obtain a lactate level in blood.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority of U.S. provisional application No.60/466,462, filed Apr. 30, 2003. The contents of the references citedthroughout the disclosure are incorporated herein by reference.

TECHNICAL FIELD

The invention relates to the measurement of blood metabolites. Moreparticularly the invention relates to the measurement of lactate usingNear-infrared (NIR) spectroscopy.

BACKGROUND OF THE INVENTION

In critical care, the continuous monitoring of blood lactate is ofsignificant importance. An increase in lactate level reflects animbalance between lactate production and elimination. Lactate can thenbe used as a marker for the assessment of tissue perfusion and oxidativecapacity. While a whole blood lactate concentration of less than 2mmol/L is considered as normal (Mizock B. A. et al., Crit. Care Med. 20:80-93, 1992), concentrations higher than 4 mmol/L have been found inassociation with myocardial infarction (R. J. et al., Circ. Shock 9:307-315, 1982), cardiac arrest (Weil M. H. et al., Crit. Care Med. 13:888-892, 1985), circulatory failure (Broder G. et al., Science 143:1457-1459, 1964; Weil M. H. et al., Circulation 41: 989-1001, 1970) andin emergency trauma situations (Aduen J. et al., JAMA 272: 1678-1685,1994, 44). Likewise, the change in pattern or the trend towards anincrease of blood lactate is a good indicator of survival (Cowan B. N.et al., Anaesthesia 39: 750-755, 1984; Vincent J. L. et al., Crit. CareMed. 11:449-451, 1983). In all these cases, measurements of lactatelevels are of prognostic significance and have to be performed by arapid and robust method.

However, most of the standard clinical methods for lactate analysis arenot adapted for continuous lactate monitoring (Baker D. A. et al, Anal.Chem. 67: 1536-1540, 1995; Soutter W. P. et al., Br. J. Anaesth. 50:445-450, 1978; Williams D. L. et al., Anal. Chem. 42; 118-121, 1970).They often require substantial sample preparations and for this reason,do not offer the possibility to the clinician of concurrent in vivo orex vivo monitoring of lactate level in a continuous manner. To achieveat patient monitoring of lactate, several in vivo biosensors, Baker D.A. et al, Anal. Chem. 67: 1536-1540, 1995; Pfeiffer D. et al., Biosens.Bioelectron. 12: 539-550, 1997; Wang D. L. et al., Anal. Chem. 65:1069-1073, 1993; Yang Q. et al., Biosens. Bioelectron. 14: 203-210,1999;ex vivo, Gfrerer R. J. et al., Biosens. Bioelectron. 13: 1271-1278,1998; Kyröläinen M. et al., Biosens. Bioelectron. 12: 1073-1081, 1997;Meyerhoff C. et al., Biosens Bioelectron. 8: 409-414, 1993; andmicrodialysis procedures Dempsey E. et al., Anal. Chim. Acta 346:341-349, 1997; Kaptein W. A. et al., Anal. Chem. 70: 4696-4700, 1998;have been developed. Although they overcome some of the problems, thesemethods suffer from several drawbacks. Biofouling, biocompatibility,thrombi formation, calculation of the recovery and discomfort for thesubjects are some of the major disadvantages and problems of thesetechniques that ultimately remain invasive devices (Ash S. R. et al.,ASAIO J. 38: M416-M420, 1992, Johne B. et al., J. Immunol. Methods 183:167-174, 1995; Justice J. B., Jr., J. Neurosci. Methods 48: 263-276,1993; Reach G. et al., Anal. Chem. 64: 381A-386, 1992).

Previous studies have shown the potential of near infrared spectroscopy(NIRS) to monitor non-invasively tissue oxygenation, Boushel R. et al.,Acta Physiol. Scand. 168: 615-622, 2000; Iwai H. et al., Ther. Res.21:1560-1564, 2000; Oda M. et al., Reza Kenkyu 25: 204-207, 1997;Thorniley M. S. et al., Biochem. Soc. Trans. 16: 978-979, 1988;Thorniley M. S. et al., Biochem. Soc. Trans. 17:903-904, 1989; and WangF. et al., Ziran Kexueban 39: 16-19, 1999; and other metabolites, ArnoldM. A., Curr. Opin. Biotechnol. 7: 46-49, 1996; Heise H. M. et al.,Artif. Organs 18: 439-447, 1994; Heise H. M., Horm. Metab. Res. 28:527-534, 1996; Heise H. M. et al., AIP Conf Proc. 430: 282-285, 1998;Heise M. et al., J. Near Infrared Spectrosc. 6: 349-359, 1998; MarbachR. M. et al., Appl. Spectrosc. 47: 875-881, 1993; Mueller U. A. et al.,Int. J. Artif. Organs 20: 285-290, 1997; and Robinson M. R. et al.,Clin. Chem. 38:1618-1622, 1992. Likewise recently, in vitro measurementof lactate was also made using Near Infrared Spectroscopy Lafrance D. etal., Appl. Spectrosc. 54: 300-304, 2000; Lafrance D. et al., Can. J.Anal. Sci. & Spectrosc. 45: 34-38, 2000.; Lafrance D. et al., Talanta(To be published).

In view of the above there is clearly a need for more effectivemeasurement methods for blood metabolites.

SUMMARY OF THE INVENTION

The present invention provides a system and method for the in vivodetermination of lactate levels in blood using Near-InfraredSpectroscopy (NIRS)and/or Near-infrared Raman Spectroscopy (NIR-RAMAN).

In one embodiment of the method, a part of the body is optically coupledwith a near infrared light source and detector. Light is injected anddetected at multiple wavelengths to produce an optical signal that canbe processed to derive levels of blood metabolites such as lactate. Themethod enables measurements of lactate to be performed more rapidly thanexisting methods and to allow continuous monitoring. Furthermore, whenthe processor is coupled to a monitor, signals perceptible to a user maybe generated to indicate lactate levels differing from predeterminedlevels. These advantages can be exploited in clinical situations orduring physiological exercises studies for example.

In a further aspect of the method NIRS may be used to measure lactatelevels in blood samples using transmission or reflectance spectroscopy.

In yet a further embodiment there is provided a system for the in vivomeasurement of lactate comprising an NIR light source, means foroptically coupling the source to a body part and means for opticallycoupling the body part to a detector, means to process the diffusereflectance optical signal to generate a measure of lactate levels andmonitoring means to compare measured lactate levels to predeterminedlevels and to trigger signals perceivable by a user when the comparedlevels are within a predetermined range.

BRIEF DESCRIPTION OF THE DRAWINGS

Further features and advantages of the present invention will becomeapparent from the following detailed description, taken in combinationwith the appended drawings, in which:

FIG. 1 is an example of a correlation coefficient plot based on diffusereflectance spectra from the fingernails of each of the subjects tested;

FIG. 2 is an example of a 2D-NIR correlation spectra (synchronous andasynchronous) based on diffuse reflectance spectra from the fingernailsof each of the subjects tested;

FIG. 3 is an example of a PRESS plot for lactate cross-validation modelbased on the 1500 to 1750 nm spectral range;

FIG. 4 is an example of a calibration coefficient plot using 4 PLSfactors for the in vivo determination of lactate;

FIG. 5 is an example of NIRS estimated vs. Kodak Vitros values for invivo lactate measurements for each of the ten subjects (Cross-validationmodel: 4 PLS factors based on 1500-1750 nm spectral segment; n=40,R²=0.74, RMSCV=2.21 using a leave-4-out cross-validation procedure);

FIG. 6 is an example of NIRS estimated vs. lactate referenced values forin vivo lactate measurements for each of the ten subjects(Cross-validation model: 5 PLS factors based on 1500-1750 nm spectralsegment; n=30, R²=0.97, RMSCV=0.76 mmol/L using a leave-4-outcross-validation procedure); and

FIG. 7 is a schematic representation of an embodiment of the system ofthe present invention.

It will be noted that throughout the appended drawings, like featuresare identified by like reference numerals.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

In one embodiment of the present invention there is provided a methodand system for the in vivo measurement of blood lactate levels using NIRreflectance spectroscopy. The method involves the optical coupling of abody part with a NIR source and a suitable detector for measuring lightexiting from the body part. By analyzing the light exiting the body atpredetermined wavelengths, the method enables the in vivo measurement ofblood lactate levels. The selection of the appropriate wavelengths willbe further described below. The non-invasive nature of the methodpermits frequent measurements of blood lactate to be made in acontinuous manner. Furthermore, by linking the lactate results outputwith a monitor device, the system and method provides a means fortriggering an alarm in response to changes in blood lactate levels.Abnormal levels may occur in individuals suffering myocardialinfarction, cardiac arrest, circulatory failure, emergency trauma andthe like or during exercises. The alarm enables one to decide whethercorrective measures should be taken.

While several parts of the body may be suitable for the acquisition ofdata, digits (fingers and toes) are preferred. More preferably the nailportion of digits is used since the nail is relatively transparent toNIR and the nail bed is rich in capillary blood vessels.

To determine the predominant change in the spectra, 2D correlationspectroscopy was used (Noda I., Bull. Am. Phys. Soc. 31: 520-552, 1986;Noda I., J. Am. Chem Soc. 111: 8116-8118, 1989). The technique of 2Dcorrelation spectroscopy was developed for characterizing differences inspectral responses between elements of a set of spectra with certainvariations present among them. Two pre-processing steps were used on thespectra before plotting the 2D correlation spectrum. First, all spectrawere mean-centered. Mean-centering emphasized the subtle variations inthe spectra due to changing species concentrations. To enhance thespectral variations of species over the background and minimize baselinevariation, the second derivative of all blood sample spectra wascalculated using discrete differences (Holler F. et al., Appl.Spectrosc. 43: 877-882, 1989).

Following the determination of the predominant species in the spectra,Partial Least Squares (PLS) regression analysis was made on thepre-processed data. The PLS method and the second derivative routinehave been developed previously and details of the algorithm have beendiscussed (Arakaki L. S. L. et al., Appl. Spectrosc. 50: 697-707, 1996;Holler F. et al., Appl. Spectrosc. 43: 877-882, 1989). For robustestimation using PLS, a cross validation method was used with 40 uniquesamples. In this study, blocks of four samples from the same volunteerwere left out. Since each individual is excluded and estimated by thenine others, the leave-one-individual-out cross-validation approachensures that variations between patients could be determined. The finalmodel is developed using ten individual calibration coefficient vectorsto estimate the concentration in each of the samples. The predictionerror sum of squares (PRESS) with F-test significance comparisons wasused to determine the minimum number of statistically significantfactors (D.M. et al., Anal. Chem. 60: 1193-1202, 1988). The number oflatent variables used in the model presented in this study is determinedusing the cumulative PRESS calculated from the sum of the tenleave-one-individual-out cross validations. In one embodiment, allprograms for input of spectral data, pre-processing, 2D correlation plotand cross-validation were written in Matlab (The Mathworks Inc., SouthNatick, Mass.). However it will be appreciated that other software maybe used which use multilinear regression to develop a calibration vectorfor lactate.

In order to determine wavelengths that mainly correlate over time withspectral changes, a correlation coefficient plot is shown in FIG. 1.Although it was not possible to assign some of the most correlatedwavelengths with a particular species (1586 nm, 1593 nm, 1626 nm and1716 nm), other correlated wavelengths can be assigned to glucose (1612nm and 1689 nm) and lactate (1675 nm, 1690 nm and 1730 nm). Nocorrelated wavelengths are related to water.

Table I shows changes over time of lactate and glucose concentration foreach of ten individuals tested at various time before and afterexercise. TABLE I Lactate and glucose concentration changes over thecourse for each of ten individuals. Lactate Glucose At (mmol/L) At(mmol/L) rest t = 0 min t = 5 min t = 10 min rest t = 0 min t = 5 min t= 10 min Subject 1 0.9 1.8 11.2 10.9 5.0 5.4 5.4 5.3 Subject 2 0.7 2.15.1 5.4 4.6 4.6 4.6 4.5 Subject 3 0.8 1.2 6.3 6.9 4.6 4.5 4.7 4.7Subject 4 0.9 1.6 6.0 5.6 4.6 4.8 5.0 4.9 Subject 5 1.0 2.0 8.0 8.3 5.15.5 5.1 5.2 Subject 6 0.9 1.6 4.8 4.9 5.2 5.2 5.3 5.3 Subject 7 1.0 2.23.1 3.2 4.5 4.4 4.6 4.6 Subject 8 1.5 1.7 5.8 5.7 5.9 5.7 5.7 5.6Subject 9 1.0 1.4 5.2 4.6 4.9 4.9 4.7 4.7 Subject 10 1.1 1.0 10.1 10.14.9 4.7 5.4 5.1

To better understand what induced spectral changes over the course oftime, 2D correlation analysis was used. FIG. 2 shows the synchronous(bottom) and asynchronous (top) 2D correlation spectra from human nailsbed. The synchronous spectrum represents the simultaneous orcoincidental changes of spectral intensity variations measured at twodifferent wavelengths during the 10 minutes interval chosen for theexperiment. The synchronous spectrum shows correlation peaks appearingat both on and off diagonal. The on-diagonal peaks or “autopeaks”correspond to the autocorrelation of a wavelength. Thus, the evaluationof the synchronous spectrum along its diagonal provides the overallextent of dynamic fluctuations in the spectral intensity. Likewise, theoff-diagonal peaks or “cross-peaks” show the simultaneous changes ofsignals that occur at two different wavelengths. The magnitude andposition of cross-peaks can then be useful to determine whethersimultaneous spectral changes in two wavelength regions are coupled(Noda I., Bull. Am. Phys. Soc. 31: 520-552, 1986; Noda I., J. Am. ChemSoc. 111: 8116-8118, 1989; Noda I. et al., Appl. Spectrosc. 54:236A-248A, 2000). The synchronous spectrum in FIG. 2 shows that thepredominant change is centered at 1662 nm, but the peak is broad: In anattempt to assign some of the features to species of interest, standardbuffered solutions were prepared. It was determined that in the selectedspectral range (1500-1750 nm) lactate shows absorption at 1675, 1690 and1730 nm, while glucose shows at 1613, 1689 and 1732 nm (Burmeister J. J.et al., Clin. Chem. 45: 1621-1627, 1999). The feature at 1662 appears tobe a combination of absorption from fingernail (1660 nm) and lactate(1675 nm). Furthermore, simultaneous changes also appear at 1710 nm and,but with opposite sign, at 1690 nm and 1735 nm. While the feature at1690 nm can be assigned to lactate, the feature at 1735 appears to be acombination of absorption from lactate (1730 nm), glucose (1732 nm) andfingernail (1740 nm).

The top part of FIG. 2 shows the asynchronous spectrum. The asynchronousspectrum represents the sequential or successive information changes inspectral intensities measured at two different wavelengths (Noda I. etal., Appl. Spectrosc. 54: 236A-248A, 2000). Unlike the synchronousspectrum, the asynchronous plot does not have autopeaks, but onlyoff-diagonal cross-peaks and is antisymmetric with respect to thecentral diagonal. Furthermore, the sign of the cross-peak can be used todetermine the sequential order of the spectral changes that occur. Apositive asynchronous cross-peaks at (λ₁, λ₂) indicates that a change atλ₁ occurred predominately before λ₂ in the sequential order of changes.In FIG. 2, out-of phase changes appear at 1636 nm, 1600 nm and 1550 nmand, but with opposite sign, at 1610 nm and 1575 nm. While the smallout-of-phase feature at 1610 nm can be assigned to glucose, the otherfeatures of the asynchronous spectrum have not been assigned, but can berelated to other species of human tissues such as proteins.

In one aspect of the invention, 2D correlation spectroscopy techniqueled to the identification of two potential species, lactate and glucosethat could be monitored through NIR fingernail diffuse reflectance. Toconfirm which one of lactate or glucose offers the best potential forestimating concentration levels of the metabolite PLS models weredetermined for both species. However, to develop an acceptable PLSmodel, no covariance between the multiple components of the samplematrix should be seen. Table II lists the correlation coefficientsbetween measured lactate, glucose and the other parameters. TABLE IICorrelation coefficients (R) calculated between lactate and othermeasured parameters. Glucose Hematocrit Temp.-finger Temp. Mouth Lactate0.2668 0.3793 0.5212 0.2441 Glucose −0.0633 0.0849 0.0181 Hematocrit0.2956 −0.1742 Temp.-finger 0.3425

No significant correlation was found between these parameters. Likewise,it has previously been shown that variable light scattering from redblood cells can be correlated with pH changes in the samples (Alam M. K.et al., Appl. Spectrosc. 53: 316-324, 1999). The correlation with pH iscaused by variations in light scatter due to red blood cells shrinkingand swelling as a function of pH (Alam M. K. et al., Appl. Spectrosc.53: 316-324, 1999). However, such correlation is usually seen inexperiments where pH variation is much larger (>1 pH unit) than in aphysiological study Lafrance D. et al., Appl. Spectrosc. 54: 300-304,2000. Furthermore, previous study has shown no correlation betweenspectral changes and pH variation in samples during a similar protocolto this study, Lafrance D. et al., Appl. Spectrosc. 54: 300-304, 2000.

As shown in FIG. 3, the minimum of the prediction error sum of squares(PRESS) plot is reached with 4 factors for lactate. This corresponds tothe standard error in the determination of lactate within the 1500 to1750 nm range. FIG. 4 shows the calibration coefficients plot based on a4 PLS model. This represents the calibration coefficients at eachwavelength, as determined by PLS. Upon viewing FIG. 4, it should benoted that the peaks magnitude are the important features, and bothpositive and negative values are significant. In the figure, the peaksat 1680 nm (lactate, fingernail), 1690 nm (lactate, glucose), 1710 nm,1725 nm (lactate, fingernail) and 1740nm (glucose, fingernail)contribute to the greatest extent to the calibration model.

Estimations of lactate concentration in whole blood were obtained by thescalar product of the calibration coefficients vector and each spectrumof the data set. Results using 4 PLS factors are shown in FIG. 5.Correlation between the data and the line of identity, resulted in acorrelation coefficient (r) of 0.74. The standard error ofcross-validation (SECV) on the linear regression was calculated to be2.21 mmol/L. The spread seen in the data possibly comes from smallvariations in blood composition or in the nail bed during exercisewithin individuals. However, as shown in a previous studies wherelactate was measured in human plasma and blood, no particular groupingin the data is seen (Lafrance D. et al., Appl. Spectrosc. 54: 300-304,2000; Lafrance D. et al., Talanta. (To be published). This considerationindicates that possible variations in blood composition betweenindividuals have little impact on the model. Likewise, FIG. 5 showedthat although tight correlation of the data is not apparent, the largechange in lactate induced by exercise is easily distinguished. This willalso be expected in illness situations.

The PLS model was also used to estimate glucose concentration. Theminimum standard error in the determination of glucose was achieved byusing thirteen factors. However, after a F-test significance comparisonwas used to determine the significant number of factors, no differencewas found statistically between thirteen and four factors. When fourfactors are used to build the PLS model for glucose, the correlationcoefficient (r) gave 0.37 and the standard error of cross-validation(SECV) on the linear regression was calculated to be 1.53 mmol/L. Thisresult indicates that from the two species, lactate is most likely to bethe one that can be monitored using the NIR diffuse reflectance indigits such as fingers.

As mentioned previously, a blood lactate concentration of less than 2mmol/L is considered as normal (Mizock B. A. et al., Crit. Care Med. 20:80-93, 1992). Therefore, lactate concentrations changes above 2 mmol/Lare particularly important to detect. The current model represents theminimum needed to monitor lactate changes that could occur around thatthreshold value. Most of the variation appears to come from baselinedifferences of blood within each of the subjects and the contribution ofthe fingernail and the fingernail bed to the spectra. To test modelswith reduced blood composition difference and fingernail contribution,spectra from volunteers at rest were subtracted from the other spectraof each volunteer with the corresponding measured lactate referenced tothe standard. This operation is equivalent to a baseline correction foreach individual, which is easily accomplished in the clinic.

The two pre-processing steps were applied on the resulting spectra andthe PLS routine was recalculated. The minimum number of PLS factors touse, calculated with an F-test at a 95% confidence level, was five. FIG.6 shows the estimations of in vivo referenced lactate concentrationsusing the 5 PLS factors. Correlation between the data and the line ofidentity gives a correlation coefficient (r) of 0.97. The standard errorof cross-validation (SECV) on the linear regression is 0.76 mmol/L. Thestandard error has decreased by a factor of three. This translates to asignificant improvement in the capability of the model to estimatelactate concentration change. These results indicate the potential ofreferenced lactate measurements for in vivo physiological or clinicalassessment when lactate change in an individual is significant.

It will be appreciated that methods other than PLS can be used todetermine the calibration coefficient. For example it may be possible touse empirically determined coefficients that provide a lactateconcentration falling in a desired range of concentrations.

In another embodiment of the invention, NIR—Raman spectroscopy may alsobe used to determine lactate levels in vivo. Thus, NIR light may beinjected at one desired wavelength and Raman-shift signals arising fromthe interaction of the injected light with lactate may be detected at aplurality of wavelengths. The optical signal thus generated may then beanalyzed as described above to determine lactate levels.

It will be appreciated that the NIR reflectance data can be acquired atpredetermined times. In particular acquisition of data can besynchronized with blood volume variations in the body part where themeasurements are taken to account for variations in the optical signalas a result of the normal variations generated by the cardiac cycle.That is to say, variations in localized blood volume arising fromvariations in the blood flow. These variations may also arise fromartificial variations in blood volume in clinical situations such asblood dialysis, surgery or the like.

In a further embodiment of the method of the present invention theoptical signal is obtained as a continuous signal over time to generatea “wave” signal pattern reflecting the changes in blood flow. Values ofthe optical signal can then be extracted at predetermined times withinthe “wave” cycle. Also, the “wave” optical signals of two or morewavelengths can be compared to provide information on the relativelevels of selected blood constituents.

In a further embodiment, levels of lactate can be obtained for thesystolic and the diastolic phase of the cardiac cycle to provide arelative optical signal independent of blood volume variations used tocalculate lactate levels. Furthermore, it is possible to use the ratioof the “wave” signal resulting from variations in blood volume to thatof a steady-state signal (a signal not sensitive to the variation inblood volume) as a way of determining the portion of the signalcontributed by blood only. This advantageously provides lactatemeasurements that are substantially independent of measurementconditions which could affect the reproducibility of the measurements.Such measurement conditions may include but are not limited to theposition of the optical coupling means on the body part, intensity ofthe source and the like.

In a further aspect of the invention there is also provided a system forthe in vivo measurement of lactate levels using NIR reflectancespectroscopy. The system comprises a NIR light source 10, means foroptically coupling 12 the light source with the body part 14 from whichthe measurements will be obtained, means for optically coupling 16 thebody part 14 with a detector 18, a processor means 20 to process theoptical signal exiting the body part and generate a lactate level orconcentration and a monitoring means 22 for comparing the measuredlactate level with predetermined values of lactate and signaling to auser any difference between the compared values. The processor means ofthe system may also process the data collected by the detector todetermine the wavelengths to be used for the measurements. Thisdetermination can be achieved as explained supra using PLS analysis forexample. The processor means may be linked to a wavelengths selector 24to control the wavelengths at which the source will illuminate the bodypart and the operational wavelengths for the detector. It will beappreciated that the detector can be selectively gated for certainpre-determined wavelengths. Alternatively the wavelength selector maycontrol wavelengths selection means such as filters for example.

The means for optically coupling may be mirrors, lenses, optic fibersand the like. The detector means may be any suitable detector operatingin the NIR region of the spectrum.

The system may also comprise a synchronizer means 26 for synchronizingthe acquisition of data with a desired event such as the cardiac cyclefor example. The synchronizer is preferably linked to the detector, thesource and the monitor and any other device that can record the eventsuch as an electrocardiograph for example.

In a further embodiment, lactate levels may also advantageously bemeasured using NIR transmission spectroscopy using blood samples. Inthis embodiment a NIR spectrum of a blood sample is obtained. Estimationof lactate concentration is then obtained by the scalar product ofpredetermined regression calibration coefficients vectors as will befurther explained below.

EXAMPLES Example 1 Sample Collection

Ten healthy adult subjects (six males and four females) were testedduring maximal effort made during a 30-s sprint on a modified isokineticcycle. The cycle was modified to have the pedal speed fixed and efforttranslated into greater force generation Lands L. C. et al., J. Appl.Physiol. 77: 2506-2510, 1994. The study was approved by the EthicsCommittee of the Montreal Children's Hospital, in accordance with theHelsinki Declaration of 1975. After signed informed consent, and priorto exercise, an intravenous line was placed in the antecubital fossa,and kept patent (open) with a 0.9% saline solution. Blood was sampled atfour time intervals: (1) just prior to exercise; (2) at the end ofexercise; (3) 5 min. following exercise; (4) 10 min. following exercise.This approach was used in an attempt to induce changes within the humanphysiological ranges for lactate, while minimizing covariance with otherspecies. Blood was drawn into tubes containing lithium heparin beads(Sarstedt Inc., St-Laurent, Quebec) and immediately transferred topre-chilled 0.75 mL microvette tubes containing 1 mg/mL of sodiumfluoride, to arrest glycolysis. Samples were then spun at 15 000 rpm atroom temperature for 5 minutes in an Eppendorf microcentrifuge Model5417C (Eppendorf Scientific, Westbury, N.Y.) to remove plasma foranalysis. Plasma samples were each assayed once on a Kodak (Vitros)Model 750 (Orthoclinical Diagnostics, Rochester, N.Y.) for lactate andglucose. Likewise, to monitor the potential impact on light scattering,blood hematocrit was measured for all samples. For the hematocritmeasurement, blood samples were placed in capillary tubes. The tubeswere loaded into a centrifuge and spun at 13000 rpm for 1 minute.Hematocrit was measured by reading the volume percentage of the redblood cells in the tubes using a micro-capillary reader.

Example 2 Data Collection

Spectra were collected with a Nicolet Magna-IR 550 Fourier transformnear-infrared (FT-NIR) spectrometer (quartz beamsplitter). Theinstrument was equipped with stabilized external quartz tungsten halogensource (300 W, Oriel) and an InSb detector. A sample holder, thatallowed the finger to rest in front of the light beam, was used tominimize finger movement during exercise and data collection. Two flatmirrors (Edmund Scientific Company, Inc., Barrington, N.J., USA) wereused in the sample compartment to bring light to the fingernail andallow diffuse reflectance NIR spectra to be obtained. The spectral rangescanned was from 1000 to 2500 nm (11500-4000cm⁻¹). A total of 64interferogram scans at a spectral resolution of 16 cm⁻¹ were averaged.Single-beam spectra were computed with a Happ-Genzel apodization andFourier transformation routines available on the system. Backgroundspectra of air were taken every hour. Skin and body temperatures weremonitored during data collection with a copper-constantan thermocoupleprobe and a thermometer (Becton and Dickinson, Mississauga, Ont.) placedrespectively in the hand and the mouth of the subject. In this study,the spectral range from 1500-1750 nm was used to do transcutaneousmeasurements. There were several reasons that motivated the choice ofdiffuse reflectance spectroscopy of the human nail bed at thesewavelengths. First, the fingernail is relatively transparent in this NIRregion with absorption near 1660 and 1740 nm (Alam M. K. et al., Appl.Spectrosc. 53: 316-324, 1999). With the low absorption, a significantportion of the reflectance signal that arises comes from the nail bed ordeeper, where the tissue is rich in capillary blood vessels (Alam M. K.et al., Appl. Spectrosc. 53: 316-324, 1999). Furthermore, theroot-mean-square (rms) noise of the 100% lines computed across the 1500-1750 nm range using a linear model is 1.38 micro Absorbance Units(μAU). The signal-to-noise ratio (SNR) at 1690 nm is approximately 20,which is sufficient to distinguish species absorption over thebackground. Finally, several species like lactate or glucose showabsorptivities of acceptable magnitude in this spectral range.

In accordance with the present invention, allow absolute measurement oflactate is also contemplated.

In tests performed, the use of a common spectrum for the relativemeasurements which would make the results absolute measurement oflactate was tested. Second, the use of ratioing to the background (as isdone in pulse oximetry) to provide a correction for each individual hasbeen tested. For these measurements, several different wavelengthregions were explored.

Method 1: Common Starting Spectrum.

In the case that a relative measurement of lactate is made for eachindividual, an initial spectrum is acquired to account to varying tissuebaselines between individuals. To provide quantitative absolutemeasurements of lactate, a common starting spectrum for all individualsis used. For this, the initial spectrum for all of nine subjects wasaveraged together with the average resting lactate level.

Subsequent spectra from the subjects were subtracted from this value andthe lactate calculated using the partial least squares method describedabove. A leave one subject out cross validation was made with theresultant estimations for lactate. Lactate estimation was possible withreasonable accuracy. Though not being quite as precise as theindividually referenced measurements, the lactate measurement wassuitable for routine monitoring.

Method 2: Ratioing Spectra to Background.

In pulse oximetry, background corrections are achieved by ratioing thespectral signal from pulsatile variation in the tissue. A similarcorrection was examined for lactate measurements. Spectra were ratioedto the initial spectrum obtained from each individual. Leave one outcalibration was then made using the ratioed spectra and a stepwisemultilinear regression which chooses the wavelengths to include in themodel which best fits the data. Results were very encouraging. Using asimilar wavelength range as the previous lactate measurements, sevenwavelengths were selected for the model. The wavelengths used were 1642nm, 1510 nm, 1689 nm, 1708 nm, 1623 nm, 1655 nm, and 1558 nm, in orderof contribution from greatest to least. Results are similar to resultsusing partial least squares. The R2 value obtained was 0.9778.Additionally, three other wavelength regions not previously reportedwere examined. The wavelength range from 2000-2400 nm gave similarthough slightly worse estimates of lactate. The choice of wavelengthswas 2088 nm, 2111 nm, 2070 nm, 2289 nm, 2325 nm, 2082 nm, and 2400 nm,again in order of contribution from greatest to least. The R2 valueobtained was 0.93841. This is probably due to the poor penetration depthof light into tissue in this region. Very good results were alsoachieved using the wavelength region 1100-1500 nm. This region of thespectra penetrates deeply into tissue and would be practical for aclinical device. The choice of wavelengths was 1468 nm, 1510 nm, 1113nm, 1239 nm, 1494 nm, 1172 nm, and 1341 nm, in order of contributionfrom greatest to least. The R2 value obtained was 0.97631. Finally,reasonable estimates were obtained using the wavelength region between1000-1100 nm. The choice of wavelengths was 1019 nm, 1011 nm, 1024 nm,1012 nm, 1058 nm, 1086 nm, and 1030 nm, in order of contribution fromgreatest to least. The R2 value obtained was 0.93789. Though the lactateestimation was not as good at in the 1100-1500 nm region, the shorterwavelength range is accessible to silicon detectors and allowsinexpensive devices to be constructed. The plurality of wavelengths maybe provided using a plurality of narrowband light sources, such as LEDs,or by using a broadband light source and filters, or by using a tunablesource. Wavelength selection may be performed at the source or at thedetector, as desired.

It will also be appreciated that the present invention may be applied tomeasure lactate levels in body fluid in vivo by measurement across theskin or in body cavities, such as orally or vaginally. In a preferredembodiment, the invention may be used in a vaginal probe to measurelactate in amniotic fluid. Using the present invention, the light sourceand detector can be provided at or optically coupled to the tip of thevaginal probe.

The embodiment(s) of the invention described above is (are) intended tobe exemplary only. The scope of the invention is therefore intended tobe limited solely by the scope of the appended claims.

1. A method for measuring lactate in vivo comprising: optically couplinga body part with a light source and a light detector said body parthaving tissues comprising blood vessels; injecting near-infrared (NIR)light at a plurality of wavelengths in said body part; detecting, as afunction of blood volume variations in said body part, light exitingsaid body part at at least said plurality of wavelengths to generate anoptical signal; processing said optical signal as a function of saidblood volume variations to obtain a lactate level in blood.
 2. A methodfor measuring lactate in vivo comprising: optically coupling a body partwith a light source and a light detector said body part having tissuescomprising blood vessels; injecting NIR light at one wavelength in saidbody part; detecting, as a function of blood volume variations in saidbody part, light exiting said body part at a plurality of wavelengths togenerate an optical signal due to a Raman shift from lactate; processingsaid optical signal as a function of said blood volume variations toobtain a lactate level in blood.
 3. A method for measuring lactate invivo comprising: optically coupling a body part with a light source anda light detector said body part having tissues comprising blood vessels;injecting near-infrared (NIR) light one or more wavelengths in said bodypart; detecting, as a function of blood volume variations in said bodypart, light exiting said body part at a plurality of wavelengths togenerate an optical signal: processing said optical signal as a functionof said blood volume variations to obtain a lactate level in blood, saidprocessing comprising: a) determining a regression calibrationcoefficient vector for each of said plurality of wavelengths; b)obtaining a scalar product from said calibration coefficient vector andan amplitude of each of said plurality of wavelengths.
 4. The method asclaimed in claim 3, wherein said plurality of wavelengths have anabsorption coefficient that is substantially independent of waterconcentration.
 5. The method as claimed in claim 4, wherein saidinjecting and said detecting is synchronized with changes in bloodvolume in said body part.
 6. The method as claimed in claim 5 whereinsaid changes in blood volumes are due to cardiac cycle.
 7. The method asclaimed in claim 6 wherein said lactate level is a relative levelbetween systolic and diastolic parts of said cardiac cycle.
 8. Themethod as claimed in claim 3, wherein said injecting and said detectingproduces a time-varying optical signal, said time-varying optical signalbeing a function of changes of blood volume in said body part.
 9. Themethod as claimed in claim 8, wherein said changes in blood volumes aredue to cardiac cycles.
 10. The method as claimed in claim 9, whereinsaid detecting comprises detecting light at said plurality ofwavelengths to generate said time-varying optical signals and a steadystate signal and wherein a ratio of said time varying optical signalsand said steady state signal is obtained to thereby producing a relativesignal substantially reflecting said lactate level in blood.
 11. Themethod as claimed in claim 3, wherein said body part is a digitcomprising a nail and a nail bed.
 12. The method as claimed in claim 11,wherein said NIR light is injected through said nail.
 13. The method asclaimed in claim 12, wherein said exiting light is detected though saidnail bed.
 14. The method as claimed in claim 13, wherein saidilluminating comprises: a) immobilizing said digit in a samplecompartment; and b) directing said NIR light on said nail.
 15. Themethod as claimed in claim 14, wherein said plurality of wavelengths isat least four.
 16. The method as claimed in claim 15, wherein said lightdetected is at a same wavelength as said light injected, and thewavelengths are 1680 nm, 1690 nm, 1710 nm and 1725 nm.
 17. The method asclaimed in claim 16, wherein a reference optical signal is subtractedfrom said optical signal.
 18. The method as claimed in claim 3, furthercomprising: activating an alarm when said lactate level differs from apredetermined level indicative of an abnormal lactate-dependentcondition; and taking at least one corrective action in response to saidabnormal lactate-dependent condition.
 19. The method as claimed in claim18, wherein said abnormal lactate-dependent condition is high lactatelevel in an exercising subject and wherein said corrective actioncomprises stopping said subject from exercising.
 20. The method asclaimed in claim 18, wherein said abnormal lactate-dependent conditionis a clinical condition in a subject selected from myocardialinfarction, cardiac arrest, circulatory failure, emergency trauma.
 21. Asystem for measuring in vivo lactate levels comprising: a NIR lightsource; a source coupler optically coupling said light source to a bodypart; detector coupler optically coupling said body part to a detectorfor measuring light exiting 'said body part and producing an opticalsignal; processor receiving said optical signal and generating ameasured lactate level value; and a monitoring device comparing saidmeasured lactate level value with at least one predetermined lactatevalue, and triggering a signal perceptible by a user when said comparedvalues are within a predetermined range.
 22. The system as claimed inclaim 21, wherein said processor determines said predeterminedwavelengths.
 23. The system as claimed in claim 22, further comprising awavelengths selector selecting said source wavelengths and said detectoroperating wavelengths.
 24. The system as claimed in claim 23, furthercomprising a synchronizer synchronizing said measuring with a desiredevent.
 25. The system as claimed in claim 24, wherein said event iscardiac cycle.
 26. The system as claimed in claim 25, wherein saidsynchronizer is operationally coupled to an electrocardiograph.